Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality

Gustavo Aguilar, Bryan McCann, Tong Niu, Nazneen Rajani, Nitish Shirish Keskar, Thamar Solorio


Abstract
Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often lead to unreasonably long sequences and make it harder for the model to learn meaningful words. To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed–and thus, providing a practical method. Finally, we show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark.
Anthology ID:
2021.findings-emnlp.141
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1640–1651
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.141
DOI:
10.18653/v1/2021.findings-emnlp.141
Bibkey:
Cite (ACL):
Gustavo Aguilar, Bryan McCann, Tong Niu, Nazneen Rajani, Nitish Shirish Keskar, and Thamar Solorio. 2021. Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1640–1651, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality (Aguilar et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.141.pdf
Data
LinCE